Low power synthesis of dual threshold voltage CMOS VLSI circuits
ISLPED '99 Proceedings of the 1999 international symposium on Low power electronics and design
A Probabilistic Approach to Buffer Insertion
Proceedings of the 2003 IEEE/ACM international conference on Computer-aided design
Proceedings of the 2004 international symposium on Low power electronics and design
Proceedings of the 42nd annual Design Automation Conference
A general framework for accurate statistical timing analysis considering correlations
Proceedings of the 42nd annual Design Automation Conference
Robust gate sizing by geometric programming
Proceedings of the 42nd annual Design Automation Conference
Probabilistic dual-Vth leakage optimization under variability
ISLPED '05 Proceedings of the 2005 international symposium on Low power electronics and design
Variability-Driven Buffer Insertion Considering Correlations
ICCD '05 Proceedings of the 2005 International Conference on Computer Design
Asymptotic probability extraction for non-normal distributions of circuit performance
Proceedings of the 2004 IEEE/ACM International conference on Computer-aided design
Variability inspired implementation selection problem
Proceedings of the 2004 IEEE/ACM International conference on Computer-aided design
A comparison of methods for the computation of affine lower bound functions for polynomials
COCOS'03 Proceedings of the Second international conference on Global Optimization and Constraint Satisfaction
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VLSI design optimization requires evaluation of different solutions, to compare superiority of one over the other. Typically, a solution is superior if it has a better associated timing and cost. In the presence of fabrication variability, the timing and cost of a solution become random variables with spatial and functional correlations. Therefore the evaluation of solutions shall be performed probabilistically to determine the probability that a solution has better cost and timing. In this paper we propose and evaluate three methods for fast and accurate probabilistic comparison of solutions: 1) regular Monte Carlo simulation (as a basis of comparison), 2) joint-pdf approximation using moment matching, and 3) bound-based Conditional Monte Carlo simulation.We integrated these methods in a variability-driven leakage optimization framework using dual threshold voltages. Experimental results show that joint-pdf based approximation is very fast, however it results in sub-optimal solutions due to lower accuracy. Conditional Monte Carlo method is on average 25 times faster than regular Monte Carlo, but slower than approximating joint-pdf. It also results in additional improvement in expected leakage, when compared to joint-pdf method. Monte Carlo simulation is extremely slow and inapplicable to an optimization framework. Deterministic approaches that are based on worst-case estimates had the highest expected leakage.